Any-to-Any
MLX
Safetensors
gemma4
mlx-vlm
rlcd
multimodal
classification
parallel-inference
image-text-to-text
audio
video
4-bit precision
Instructions to use larkooo/gemma-e2b-rlcd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use larkooo/gemma-e2b-rlcd with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download larkooo/gemma-e2b-rlcd --local-dir gemma-e2b-rlcd
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 872 Bytes
53e24ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"validated_at_utc": "2026-09-18T18:00:36.256671+00:00",
"version": "0.4.0",
"hardware": {
"chip": "Apple M5",
"unified_memory_bytes": 34359738368
},
"automated_tests": {
"passed": 50,
"failed": 0,
"seconds": 1.25,
"command": "python -m pytest tests -q -o cache_dir=work/pytest-cache"
},
"ruff_format": "27 source, test, and script files unchanged",
"ruff_check": "passed",
"installed_source_files_verified": 11,
"cached_cli": "passed text Choice and Independent presence checks using default answer gathering",
"head_cli": "full-depth checkpoint loads and returns valid typed distributions; its animal prediction is wrong and this is not quality acceptance",
"paired_execution_winners_preserved": 48,
"full_depth_encoder_exact_matches": 4,
"calibration_validated": false,
"accuracy_improvement_established": false
}
|